Thesis Abstract

“The transport system consists of complex microscopic and macroscopic interactions that affect our transport choices, spatial planning, economic activities, safety, CO2 emissions and much more. A structured and well-founded management of such a system is therefore extremely important. This thesis focuses on unexpected and unwanted demand fluctuations that we often observe in the network due to special events. These traffic anomalies lead to system failures and cost implications. With the proposed frameworks we firstly investigate why traffic congestion occurs as well as why demand fluctuates on days when there were no apparent reasons for such phenomena. Then we propose a demand prediction solution that is based on automatic internet search queries generation. A particular emphasis is given to special events that are publicly disclosed on social media and attract many people. It is methodically investigated which types of events affect more drastically transport system’s balance, as well as how we can fast and accurately identify them. Afterwards, the knowledge gained from the previous stages is further exploited for the formulation of a real-time demand prediction model that is able to forecast taxi demand using special events’ data around venues. Framework’s structure has been thoroughly studied, while its performance is evaluated using various machine learning techniques. The proposed models highlight the value of data fusion of text and time series data, as well as the significance of information retrieval to models’ performance enhancement. They can be of great value to a broad range of traffic incidents’ management frameworks.”